Video fingerprinting is a type of a digital watermark utilized by content providers to protect their copyrighted video assets without the benefit of Digital Rights Management (DRM) or other proactive methods of providing security for video content. Video content providers will broadcast and/or distribute their content through traditional real-time broadcasting systems, such as satellite or cable television, or as files or streaming media across the Internet. Viewers can utilize third party devices, such as a Data Video Recorder (DVR), for example a TiVo, to record and store broadcasts, while other system exist for saving streaming media to a file or copying a video file from another network location. Peer to peer (P2P) programs exist for enabling decentralized sharing of files regardless of their source or copyright status. Video fingerprinting has been designed to enable a content provider to generate a unique fingerprint based on processing the entire broadcast or small portions of it. The fingerprint can then be used by processing suspect video sources for a fingerprint match between the original source and the suspect copy to ascertain occurrences of unauthorized duplication of the original source content.
Copyright infringement is not the only goal of video fingerprinting as such fingerprinting methods and algorithms are utilized to review whether source and copied video are different. Fingerprinting systems and methods that are designed to detect and determine differences in video and audio characteristics between original or authorized media content and copied or duplicated or otherwise modified media content often employ fingerprinting technology. Media files can be defined by inherent characteristics such as frame rates for the video portion of media files and bitrates for the audio portion of media files. Fingerprinting can analyze the differences in video frame rates and/or audio bitrates from separate media sources and ascertain the extent of matching of frame rates and/or bitrates between separate media sources. That is, the fingerprint matching can be defined in terms of a percent hit rate of the original media content fingerprint to the fingerprint found from a media copy. Such fingerprinting systems and methods often cannot be readily adapted to accommodate the large modifications to the original media broadcast which typical do not occur when the video source and/or audio sources are being shared by the content provider, but when the video source is being broadcast may become substantially modified.
Bookmarks are frame accurate pointers to a location within a video source. They are also relational to other bookmarks and carry some additional meta-data that can be utilized later by various other systems utilizing the bookmark at a later time. Unlike just a video fingerprint, consider a bookmark which is during a commercial and another bookmark during the same commercial later in the video source. Today's fingerprinting algorithms, designed to combat copyright infringement, would consider the video surrounding these bookmarks identical and the resulting fingerprint would be the same. Additionally by sharing the resulting video fingerprint from today's system there would be no way of knowing which one came first, the time between bookmarks or how to utilize them on a copy of the video source to regenerate a frame accurate pointer into the video with associated metadata.
The most public solution at this time is the YouTube video fingerprinting system. This system requires the original content owner to first allow the YouTube fingerprinting engine to scan all of their video assets. Then the engine scans all video files posted to the website for fingerprint matches to content owned by a content provider. The other most public solution is one being developed by Philips Electronics.
All of the current systems require all of the original video sources to be digested and are not designed to work with, for example, sporting events as these video sources vary dramatically from region to region. Essentially these systems rely heavily on the idea that the content owner has a copy of all of the versions of the content that have been distributed or provided to the public. Technology is making it possible for a content provider to syndicate their video content to multiple geographic locations while enabling each individual broadcaster of the content to tailor the final visible result based on their own liking. This means that there can be very large differences in timelines, as commercial times can vary and graphical overlays, as each broadcaster can have radically different methods of showing sports scores. Other issues break today's systems as each rebroadcast of a live feed can cause differences in the video source such as brightness, cropping, frames per second and bitrates. Today's systems expect the source fed into the fingerprinting engine be as close as possible to the source being shared publicly. Although there are some tolerances built in for contrast, brightness, bitrates and cropping, the range of accepted deviations for these data points is small as modifications to the sources are typical cutting out video from the larger video segment or transcoding the source, neither of which produce the differences which can be caused by local broadcasters modification, one or more hardware retransmissions and a wide variety of set top boxes all with highly varying video aspect ratios and digital to analog hardware components. An additional shortcoming of the other solutions is that they attempt to generate fingerprints for the entire video and as many fingerprints as possible for smaller sub-segments allowing their comparing systems to find copies of video even if the resulting copy is only a few seconds from a source that could be many hours long. To do this the systems are required to generate as many fingerprints as possible based on how the fingerprint engine is configured. Thus there is a tradeoff between having too many fingerprints and not being able to store them all versus the smallest segment time the content provider would like to detect.
There is a need for a system and method that goes beyond establishing copyright infringement occurrences.